Data Science For Civil Engineering

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Data Science for Civil Engineering

Author : Rakesh K. Jain
Publisher : CRC Press
ISBN 13 : 1000873463
Total Pages : 251 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Data Science for Civil Engineering by : Rakesh K. Jain

Download or read book Data Science for Civil Engineering written by Rakesh K. Jain and published by CRC Press. This book was released on 2023-05-10 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains use of data science-based techniques for modeling and providing optimal solutions to complex problems in civil engineering. It discusses civil engineering problems like air, water and land pollution, climate crisis, transportation infrastructures, traffic and travel modes, mobility services, and so forth. Divided into two sections, the first one deals with the basics of data science and essential mathematics while the second section covers pertinent applications in structural and environmental engineering, construction management, and transportation. Features: Details information on essential mathematics required to implement civil engineering applications using data science techniques. Discusses broad background of data science and its fundamentals. Focusses on structural engineering, transportation systems, water resource management, geomatics, and environmental engineering. Includes python programming libraries to solve complex problems. Addresses various real-world applications of data science based civil engineering use cases. This book aims at senior undergraduate students in Civil Engineering and Applied Data Science.

Probabilistic Machine Learning for Civil Engineers

Author : James-A. Goulet
Publisher : MIT Press
ISBN 13 : 0262538709
Total Pages : 298 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Probabilistic Machine Learning for Civil Engineers by : James-A. Goulet

Download or read book Probabilistic Machine Learning for Civil Engineers written by James-A. Goulet and published by MIT Press. This book was released on 2020-04-14 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises. This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises. Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book draws. The book presents key approaches in the three subfields of probabilistic machine learning: supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probability theory. It goes on to present Bayesian estimation, which is behind the formulation of both supervised and unsupervised learning methods, and Markov chain Monte Carlo methods, which enable Bayesian estimation in certain complex cases. The book then covers approaches associated with supervised learning, including regression methods and classification methods, and notions associated with unsupervised learning, including clustering, dimensionality reduction, Bayesian networks, state-space models, and model calibration. Finally, the book introduces fundamental concepts of rational decisions in uncertain contexts and rational decision-making in uncertain and sequential contexts. Building on this, the book describes the basics of reinforcement learning, whereby a virtual agent learns how to make optimal decisions through trial and error while interacting with its environment.

Data Analysis in Pavement Engineering

Author : Qiao Dong
Publisher : Elsevier
ISBN 13 : 0443159297
Total Pages : 378 pages
Book Rating : 4.4/5 (431 download)

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Book Synopsis Data Analysis in Pavement Engineering by : Qiao Dong

Download or read book Data Analysis in Pavement Engineering written by Qiao Dong and published by Elsevier. This book was released on 2023-11-06 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analysis in Pavement Engineering: Theory and Methodology offers a complete introduction to the basis of the finite element method, covering fundamental theory and worked examples in the detail required for readers to apply the knowledge to their own engineering problems and understand more advanced applications. This edition sees the significant addition of content addressing coupling problems, including Finite element analysis formulations for coupled problems; Details of algorithms for solving coupled problems; and Examples showing how algorithms can be used to solve for piezoelectricity and poroelasticity problems. Focusing on the core knowledge, mathematical and analytical tools needed for successful application, this book represents the authoritative resource of choice for graduate-level students, researchers and professional engineers involved in finite element-based engineering analysis. This book is the first comprehensive resource to cover all potential scenarios of data analysis in pavement and transportation infrastructure research, including areas such as materials testing, performance modeling, distress detection, and pavement evaluation. It provides coverage of significance tests, design of experiments, data mining, data modeling, and supervised and unsupervised machine learning techniques. It summarizes the latest research in data analysis within pavement engineering, encompassing over 300 research papers. It delves into the fundamental concepts, elements, and parameters of data analysis, empowering pavement engineers to undertake tasks typically reserved for statisticians and data scientists. The book presents 21 step-by-step case studies, showcasing the application of the data analysis method to address various problems in pavement engineering and draw meaningful conclusions.

Data Analytics for Engineering and Construction Project Risk Management

Author : Ivan Damnjanovic
Publisher : Springer
ISBN 13 : 3030142515
Total Pages : 379 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Data Analytics for Engineering and Construction Project Risk Management by : Ivan Damnjanovic

Download or read book Data Analytics for Engineering and Construction Project Risk Management written by Ivan Damnjanovic and published by Springer. This book was released on 2019-05-23 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I the authors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects. The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution.

A Primer on Machine Learning Applications in Civil Engineering

Author : Paresh Chandra Deka
Publisher : CRC Press
ISBN 13 : 0429836651
Total Pages : 201 pages
Book Rating : 4.4/5 (298 download)

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Book Synopsis A Primer on Machine Learning Applications in Civil Engineering by : Paresh Chandra Deka

Download or read book A Primer on Machine Learning Applications in Civil Engineering written by Paresh Chandra Deka and published by CRC Press. This book was released on 2019-10-28 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included. Features Exclusive information on machine learning and data analytics applications with respect to civil engineering Includes many machine learning techniques in numerous civil engineering disciplines Provides ideas on how and where to apply machine learning techniques for problem solving Covers water resources and hydrological modeling, geotechnical engineering, construction engineering and management, coastal and marine engineering, and geographical information systems Includes MATLAB® exercises

Structural Health Monitoring Based on Data Science Techniques

Author : Alexandre Cury
Publisher : Springer Nature
ISBN 13 : 3030817164
Total Pages : 490 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Structural Health Monitoring Based on Data Science Techniques by : Alexandre Cury

Download or read book Structural Health Monitoring Based on Data Science Techniques written by Alexandre Cury and published by Springer Nature. This book was released on 2021-10-23 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.

Big Data in Engineering Applications

Author : Sanjiban Sekhar Roy
Publisher : Springer
ISBN 13 : 9811084769
Total Pages : 381 pages
Book Rating : 4.8/5 (11 download)

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Book Synopsis Big Data in Engineering Applications by : Sanjiban Sekhar Roy

Download or read book Big Data in Engineering Applications written by Sanjiban Sekhar Roy and published by Springer. This book was released on 2018-05-02 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.

Data Engineering and Data Science

Author : Kukatlapalli Pradeep Kumar
Publisher : John Wiley & Sons
ISBN 13 : 1119841976
Total Pages : 367 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Data Engineering and Data Science by : Kukatlapalli Pradeep Kumar

Download or read book Data Engineering and Data Science written by Kukatlapalli Pradeep Kumar and published by John Wiley & Sons. This book was released on 2023-08-29 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.

The Science and Technology of Civil Engineering Materials

Author : J. Francis Young
Publisher : Pearson
ISBN 13 :
Total Pages : 406 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis The Science and Technology of Civil Engineering Materials by : J. Francis Young

Download or read book The Science and Technology of Civil Engineering Materials written by J. Francis Young and published by Pearson. This book was released on 1998 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: For one/two-term courses in Introductory Engineering Materials in departments of civil engineering. Applies the rigor of material science principles to a comprehensive, integrative exploration of the science and technology of construction materials.

Getting Started with Data Science

Author : Murtaza Haider
Publisher : IBM Press
ISBN 13 : 0133991237
Total Pages : 942 pages
Book Rating : 4.1/5 (339 download)

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Book Synopsis Getting Started with Data Science by : Murtaza Haider

Download or read book Getting Started with Data Science written by Murtaza Haider and published by IBM Press. This book was released on 2015-12-14 with total page 942 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.

Foundations of Data Science for Engineering Problem Solving

Author : Parikshit Narendra Mahalle
Publisher : Springer Nature
ISBN 13 : 9811651604
Total Pages : 125 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Foundations of Data Science for Engineering Problem Solving by : Parikshit Narendra Mahalle

Download or read book Foundations of Data Science for Engineering Problem Solving written by Parikshit Narendra Mahalle and published by Springer Nature. This book was released on 2021-08-21 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.

Computing in Civil Engineering 2019

Author : Yong K. Cho
Publisher :
ISBN 13 : 9781523129270
Total Pages : 621 pages
Book Rating : 4.1/5 (292 download)

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Book Synopsis Computing in Civil Engineering 2019 by : Yong K. Cho

Download or read book Computing in Civil Engineering 2019 written by Yong K. Cho and published by . This book was released on 2019 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: Selected papers from the ASCE International Conference on Computing in Civil Engineering 2019, held in Atlanta, Georgia, June 17-19, 2019. Sponsored by the Computing Division of ASCE.This collection contains 77 peer-reviewed papers on data, sensing, and analytics in civil engineering.Topics include: big data and machine learning; reality capture technologies; LiDAR and RGB-D; and robotics, automation, and control.This proceedings will be of interest to researchers and practitioners working with emerging computing technologies in a wide range of civil and construction engineering applications.

Big Data Analytics in Traffic and Transportation Engineering: Emerging Research and Opportunities

Author : Moridpour, Sara
Publisher : IGI Global
ISBN 13 : 1522579443
Total Pages : 188 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Big Data Analytics in Traffic and Transportation Engineering: Emerging Research and Opportunities by : Moridpour, Sara

Download or read book Big Data Analytics in Traffic and Transportation Engineering: Emerging Research and Opportunities written by Moridpour, Sara and published by IGI Global. This book was released on 2019-01-11 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent research reveals that socioeconomic factors of the neighborhoods where road users live and where pedestrian-vehicle crashes occur are important in determining the severity of the crashes, with the former having a greater influence. Hence, road safety countermeasures, especially those focusing on the road users, should be targeted at these high risk neighborhoods. Big Data Analytics in Traffic and Transportation Engineering: Emerging Research and Opportunities is an essential reference source that discusses access to transportation and examines vehicle-pedestrian crashes, specifically in relation to socioeconomic factors that influence them, main predictors, factors that contribute to crash severity, and the enhancement of pedestrian safety measures. Featuring research on topics such as public transport, accessibility, and spatial distribution, this book is ideally designed for policymakers, transportation engineers, road safety designers, transport planners and managers, professionals, academicians, researchers, and public administrators.

Statistics and Data Analysis for Engineers and Scientists

Author : Tanvir Mustafy
Publisher : Springer Nature
ISBN 13 : 9819946611
Total Pages : 190 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Statistics and Data Analysis for Engineers and Scientists by : Tanvir Mustafy

Download or read book Statistics and Data Analysis for Engineers and Scientists written by Tanvir Mustafy and published by Springer Nature. This book was released on 2024-02-11 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook summarizes the different statistical, scientific, and financial data analysis methods for users ranging from a high school level to a professional level. It aims to combine the data analysis methods using three different programs—Microsoft Excel, SPSS, and MATLAB. The book combining the different data analysis tools is a unique approach. The book presents a variety of real-life problems in data analysis and machine learning, delivering the best solution. Analysis methods presented in this book include but are not limited to, performing various algebraic and trigonometric operations, regression modeling, and correlation, as well as plotting graphs and charts to represent the results. Fundamental concepts of applied statistics are also explained here, with illustrative examples. Thus, this book presents a pioneering solution to help a wide range of students, researchers, and professionals learn data processing, interpret different findings derived from the analyses, and apply them to their research or professional fields. The book also includes worked examples of practical problems. The primary focus behind designing these examples is understanding the concepts of data analysis and how it can solve problems. The chapters include practice exercises to assist users in enhancing their skills to execute statistical analysis calculations using software instead of relying on tables for probabilities and percentiles in the present world.

Principles of Strategic Data Science

Author : Dr Peter Prevos
Publisher : Packt Publishing Ltd
ISBN 13 : 1838985506
Total Pages : 104 pages
Book Rating : 4.8/5 (389 download)

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Book Synopsis Principles of Strategic Data Science by : Dr Peter Prevos

Download or read book Principles of Strategic Data Science written by Dr Peter Prevos and published by Packt Publishing Ltd. This book was released on 2019-06-03 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take the strategic and systematic approach to analyze data to solve business problems Key FeaturesGain detailed information about the theory of data scienceAugment your coding knowledge with practical data science techniques for efficient data analysis Learn practical ways to strategically and systematically use dataBook Description Principles of Strategic Data Science is created to help you join the dots between mathematics, programming, and business analysis. With a unique approach that bridges the gap between mathematics and computer science, this book takes you through the entire data science pipeline. The book begins by explaining what data science is and how organizations can use it to revolutionize the way they use their data. It then discusses the criteria for the soundness of data products and how to best visualize information. As you progress, you’ll discover the strategic aspects of data science by learning the five-phase framework that enables you to enhance the value you extract from data. The final chapter of the book discusses the role of a data science manager in helping an organization take the data-driven approach. By the end of this book, you’ll have a good understanding of data science and how it can enable you to extract value from your data. What you will learnGet familiar with the five most important steps of data scienceUse the Conway diagram to visualize the technical skills of the data science teamUnderstand the limitations of data science from a mathematical and ethical perspectiveGet a quick overview of machine learningGain insight into the purpose of using data science in your workUnderstand the role of data science managers and their expectationsWho this book is for This book is ideal for data scientists and data analysts who are looking for a practical guide to strategically and systematically use data. This book is also useful for those who want to understand in detail what is data science and how can an organization take the data-driven approach. Prior programming knowledge of Python and R is assumed.

Probabilistic Machine Learning

Author : Kevin P. Murphy
Publisher : MIT Press
ISBN 13 : 0262369303
Total Pages : 858 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Probabilistic Machine Learning by : Kevin P. Murphy

Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2022-03-01 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

Artificial Intelligence Applications for Sustainable Construction

Author : Moncef L. Nehdi
Publisher : Elsevier
ISBN 13 : 0443131929
Total Pages : 440 pages
Book Rating : 4.4/5 (431 download)

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Book Synopsis Artificial Intelligence Applications for Sustainable Construction by : Moncef L. Nehdi

Download or read book Artificial Intelligence Applications for Sustainable Construction written by Moncef L. Nehdi and published by Elsevier. This book was released on 2024-02-13 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence Applications for Sustainable Construction presents the latest developments in AI and ML technologies applied to real-world civil engineering concerns. With an increasing amount of attention on the environmental impact of every industry, more construction projects are going to require sustainable construction practices. This volume offers research evidence, simulation results, and case studies to support this change. Sustainable construction, in fact, not only uses renewable and recyclable materials when building new structures or repairing deteriorating ones, but also adopts all possible methods to reduce energy consumption and waste. The concisely written but comprehensive, practical knowledge put forward by this international group of highly specialized editors and contributors will prove to be beneficial to engineering students and professionals alike. Presents convincing “success stories that encourage application of AI-powered tools to civil engineering Provides a wealth of valuable technical information to address and resolve many challenging construction problems Illustrates the most recent shifts in thinking and practice for sustainable construction